SIAP meeting : Industrial Image Analysis
Wednesday, 6th December 2000 2-5pm
RSS, 12 Errol Street, London EC1Y 8LX
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TALK 1: Some problems in the study of magnetic materials
D. M. Titterington (University of Glasgow)
Abstract: The talk will cover a few aspects of the behaviour of
thin-film magnetic materials under the influence of an external field.
The materials themselves are used in such applications as modern
information storage devices and in recording heads in hard disk drives.
A short introduction to the elements of the underlying physics will be
attempted, followed by descriptions of stochastic models for dynamic
magnetisation reversal and of an empirical application of simultaneous
autoregression models to analyse images of so-called magnetisation
ripple. The work is the consequence of collaboration with colleagues in
statistics and physics.
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TALK 2: Image Analysis within Unilever Research: Quantifying
Product Microstructure
Geert M.P. van Kempen (Unilever Research)
Abstract: It is widely believed that the microstructure of the
composites typical of Unilever products plays a key role in the
manifestation of the physical observables and behaviour of the
materials and ultimately the consumer experience with the product. This
belief has lead to a need for superior microstructural understanding in
Unilever?s research activities, with the expectation that this will
lead to superior product innovation and design. This presentation will
give an overview of our efforts to quantify product microstructures and
to discriminate between microstructures of products classes that differ
on the basis of processing, ingredients or bulk physical properties.
These efforts can be divided in three parts: the translation of
microstructural attributes in parameters that can be measured by image
analysis, classification of the microstructures on the basis of the
measured parameters, and the identification and quantification of the
found differences. In the first part of the presentation, the detection
of minute differences in the microstructure of milk gels will be used
to illustrate the concepts of the microstructure quantification toolbox
we are currently developing. In the second part of the presentation,
the focus will be on the development of advanced image analysis
procedures to measure microstructure parameters. The measurement of the
anisotropy and pore-sizes of monoglyceride gels demonstrate multi-scale
image analysis. Both the monitoring of the dynamics of phase separation
as well as the measurement of the gas cell size distribution during the
proofing of dough will illustrate our efforts in time-resolved image
analysis. Finally, the segmentation of the various phases inside a
detergent powder particle shows the potential of multi-spectral
analysis for microstructure quantification. In this particular case, we
have developed a combined classifier, based on the EM algorithm, that
uses information from both the spectral as well as the spatial domain
to obtain improved segmentation results.
The image analysis developments discussed in this presentation are
developed in close collaboration with the Pattern Recognition Group of
the Delft University of Technology.
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TALK 3: Industrial Image Analysis: On the interaction between
research and application
Bjarne Kjaer Ersboell (Technical University of Denmark)
Abstract: An element of research is of paramount importance for an
industrial image analysis project to be interesting for a university
department. The Section for Image Analysis has been involved in
numerous industrial image analysis projects during the last twenty
years. All are characterised by the research being application driven
rather than method driven. In the process of solving the problem new
theoretical aspects which need to be solved inevitably turn up. We find
this interaction between theory and practice extremely inspiring. In
the talk a small representative selection of industrial image analysis
projects will be shown with special emphasis on the research
performed. Finally, we will focus on a project on sorting of
wood-slabs using image analysis. Here a combination of traditional
statistical techniques together with some novel theory on feature
selection was necessary in order to solve the problem.
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